Artificial Intelligence in Pharmaceutical Management Education: Opportunities, Challenges, and Impact

DOI:

https://doi.org/10.37285/ijpsn.2024.17.6.6%20

Authors

  • Pushparaj Patel IIHMR University, Jaipur
  • Disha Kumari MBA(Health and Hospital Management), IIHMR University, Jaipur, Rajasthan
  • Archita Jain MBA(Health and Hospital Management), IIHMR University, Jaipur, Rajasthan
  • Riya Gupta MBA(Health and Hospital Management), IIHMR University, Jaipur, Rajasthan
  • Hemanta Kumar Mishra Assistant Professor, IIHMR University, Jaipur, Rajasthan

Abstract

Background: The use of Artificial intelligence (AI) is used nowadays rigorously in the  pharmaceutical industry however, challenges remain in pharmaceutical  management education, which prepares the professional who manages the  industry. Through AI, the pharmaceutical industry designs drug discovery, formula  development, marketing, strategies, quality assurance and many more. However, in  literature, uses of AI in pharmaceutical management education have not been  widely used and discussed with reference to India.  

Objective: This article explores the opportunity of key publication, its citation, gaps,  and future scope of application of AI in the pharmaceutical industry as well as  education that how AI could help professionals who pursue pharmaceutical  management as a career in higher education. Moreover, the use of this paper will  focus on how AI can facilitate pharmaceutical management education by adopting  ethical guidelines and keeping scientific practices.  

Materials and Methods: To answer this, a systematic literature review with the  SCOPUS database from 2013 to 2023 was conducted and selected 988 research  articles out of 5,39,874 by applying the PRISMA approach. The keywords used to  search the articles are pharmaceutical, education, artificial intelligence, marketing,  strategy, future, business, management, accounting, pharmacology, toxicology, and  pharmaceutics and trends. As an inclusion criterion, articles authored by Indian  academicians in English languages were included.  

Result: The findings suggested that the role of AI in higher education is need of the  hour as industries are looking for professionals with such skills. The study also  concludes that AI in higher education could be used how to ensure customer  preference through unstructured data for selecting the best segment,  standardisation of products, regulatory approval, designing good research design in  conducting clinical trials, strategies, and marketing. These identified topics could  enrich the content of the pharmaceutical need and bring a revolution in the  pharmaceutical industry for the betterment of society.  

Conclusion: Currently, pharmaceutical management education needs more  professionals aligned with the uses of AI for developing strategies in marketing,  branding, and product development. Further, it could be used to measure customer  satisfaction and ethical regulations. Hence study recommends that curriculums  need to be looked at from various angles.  

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Keywords:

Artificial Intelligence, Pharmaceutical management education, strategy, marketing, Quality, Challenges

Published

2024-12-15

How to Cite

1.
Patel P, Kumari D, Jain A, Gupta R, Mishra HK. Artificial Intelligence in Pharmaceutical Management Education: Opportunities, Challenges, and Impact. Scopus Indexed [Internet]. 2024 Dec. 15 [cited 2025 Jan. 18];17(6):7697-705. Available from: https://ijpsnonline.com/index.php/ijpsn/article/view/4844

References

Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today. 2021 Jan 1;26(1):80.

Bhatt A. Artificial intelligence in managing clinical trial design and conduct: Man, and machine still on the learning curve? Perspect Clin Res. 2021 Jan 1;12(1):1.

Mouloudj K, Le VLO, Bouarar A, Bouarar AC, Asanza DM, Srivastava M. Adopting artificial intelligence in healthcare: A narrative review. The Use of Artificial Intelligence in Digital Marketing: Competitive Strategies and Tactics. 2023 Nov 17;1–20.

Younis HA, Eisa TAE, Nasser M, Sahib TM, Noor AA, Alyasiri OM, et al. A Systematic Review and Meta Analysis of Artificial Intelligence Tools in Medicine and Healthcare: Applications, Considerations, Limitations, Motivation and Challenges. Diagnostics. 2024 Jan 1;14(1):109.

Bedenkov A, Rajadhyaksha V, Beekman M, Moreno C, Fong PC, Agustin L, et al. Developing Medical Affairs Leaders Who Create the Future. Pharmaceut Med. 2020 Oct 1;34(5):301–7.

Carlsson C. Decision analytics—Key to digitalisation. Inf Sci (N Y). 2018 Sep 1;460–461:424–38.

Honavar VG. Artificial intelligence: An overview. 2006. 8. Jan Z, Ahamed F, Mayer W, Patel N, Grossmann G, Stumptner M, et al. Artificial intelligence for industry 4.0: Systematic review of applications, challenges, and opportunities. Expert Syst Appl. 2023 Apr 15;216:119456. 9. Herhausen D, Bernritter SF, Ngai EWT, Kumar A, Delen D. Machine learning in marketing: Recent progress and

future research directions. J Bus Res. 2024 Jan 1;170:114254.

Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019 Jun;6(2):94–8.

Malviya N, Malviya S, Dhere M. Transformation of Pharma Curriculum as Per the Anticipation of Pharma Industries-Need to Empower Fresh Breeds with Globally Accepted Pharma Syllabus, Soft Skills, AI and Hands-on Training. Indian Journal of Pharmaceutical Education and Research. 2023 Apr 1;57(2):320–8.

Ascarza E. Retention Futility: Targeting High-Risk Customers Might be Ineffective. https://doi.org/101509/jmr160163. 2018 Feb 1;55(1):80–98.

Ribeiro J, Lima R, Eckhardt T, Paiva S. Robotic Process Automation and Artificial Intelligence in Industry 4.0 – A Literature review. Procedia Comput Sci. 2021 Jan 1;181:51–8.

Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. Artificial Intelligence in Healthcare. 2020 Jan 1;25.

Tripathi S, Singh G, Kumar R, Sharma K. Artificial Intelligence In Pharma Processing. J Emerg Technol Innov Res. 2021;8(2):1987–98.

Adam G, Rampášek L, Safikhani Z, Smirnov P, Haibe Kains B, Goldenberg A. Machine learning approaches to drug response prediction: challenges and recent progress. npj Precision Oncology 2020 4:1. 2020 Jun 15;4(1):1–10.

Mak KK, Pichika MR. Artificial intelligence in drug development: present status and future prospects. Drug Discov Today. 2019 Mar 1;24(3):773–80.

Delso G, Cirillo D, Kaggie JD, Valencia A, Metser U, Veit Haibach P. How to Design AI-Driven Clinical Trials in Nuclear Medicine. Semin Nucl Med. 2021 Mar 1;51(2):112–9.

Sharma P, Sharma G, Singh M, Sharma K, Kour N, Chadha P. Applications of Artificial Intelligence in Modern Health Care and Its Future Scope. Society 50 and the Future of Emerging Computational Technologies. 2022 May 17;97–122.

Kapasia N, Paul P, Roy A, Saha J, Zaveri A, Mallick R, et al. Impact of lockdown on learning status of undergraduate and postgraduate students during COVID 19 pandemic in West Bengal, India. Child Youth Serv Rev. 2020 Sep 1;116:105194.

Mishra L, Gupta T, Shree A. Online teaching-learning in higher education during lockdown period of COVID-19 pandemic. International Journal of Educational Research Open. 2020 Jan 1;1:100012.

Bhattamisra SK, Banerjee P, Gupta P, Mayuren J, Patra S, Candasamy M. Artificial Intelligence in Pharmaceutical and Healthcare Research. Big Data and Cognitive Computing 2023, Vol 7, Page 10. 2023 Jan 11;7(1):10.

Sunarti S, Fadzlul Rahman F, Naufal M, Risky M, Febriyanto K, Masnina R. Artificial intelligence in healthcare: opportunities and risk for future. Gac Sanit. 2021 Jan 1;35 Suppl 1:S67–70.

Shah N, Kumari M, Sadhu P, Talele C. Artificial Intelligence in Pharma Industry - A Review. Asian Journal of Pharmaceutics (AJP). 2023 Jun 15;17(2):173.